Why Healthcare requires Data Anonymization?
Data Anonymization is used for protecting the privacy of the user’s data. It helps to remove personally identifiable data from the database which helps users to remain anonymous. Also, it eases the transfer of data within or outside the organization for analysis or for some other useful third-party operations.
But why not just take consent from the user, to begin with? This will surely reduce the complexity of anonymizing the data. But is it really feasible to ask for consent for every secondary operation or piece of information I am going to collect? Surely Not!!
I can have millions of secondary operations and data to be collected on an everyday basis since healthcare revolves around data and the advent of AI technologies has elevated its importance to the next level. That’s why anonymization comes into the picture.
How to do Anonymization?
Now, how to do anonymization? And is true anonymization really possible? So, 3 attributes should be kept in mind while dealing with anonymizing any data:
- Singling out every individual
- Linkability. It is linking records pertaining to an individual within one or multiple datasets
- Inference should be possible for each and every dataset
Anonymization is mainly done by using randomization and generalization. Randomization is done using techniques like noise addition and permutation while generalization is done using k-anonymity and l-diversity.
There are many examples where organizations have invested hugely in acquiring anonymized data for levelling up their work and yielding better solutions as a result in the form of a product or in order to improve the general algorithm.
IBM spent approximately USD 4 billion on acquisitions in order to enhance its Watson Health AI offerings. For example, they paid USD 30 per patient record for an anonymised pool of approximately 30 billion medical images, including x-rays, computerised axial tomography (CAT) and magnetic resonance imaging (MRI) scans.
Also, one of the major advantages of anonymization is it gives the ability to mask the original data. Thus, the organization need not create multiple copies of the original data and thus it reduces the overall cost and also it avoids large data breaches as we have seen recently how almost all of the big data breaches are on the healthcare database itself.
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